{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T23:59:26Z","timestamp":1784591966587,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:00:00Z","timestamp":1604361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After having been introduced to sales and marketing departments, artificial intelligence is also starting to guide employee-related decisions within HR management. The purpose is to support decisions that are based not on subjective aspects but on objective data analysis. The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker\u2019s decision to leave a company, and to be able to predict whether a particular employee will leave the company. After the training, the obtained model for the prediction of employees\u2019 attrition is tested on a real dataset provided by IBM analytics, which includes 35 features and about 1500 samples. Results are expressed in terms of classical metrics and the algorithm that produced the best results for the available dataset is the Gaussian Na\u00efve Bayes classifier. It reveals the best recall rate (0.54), since it measures the ability of a classifier to find all the positive instances and achieves an overall false negative rate equal to 4.5% of the total observations.<\/jats:p>","DOI":"10.3390\/computers9040086","type":"journal-article","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T09:09:32Z","timestamp":1604394572000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":217,"title":["Predicting Employee Attrition Using Machine Learning Techniques"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3288-044X","authenticated-orcid":false,"given":"Francesca","family":"Fallucchi","sequence":"first","affiliation":[{"name":"Department of Innovation &amp; Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"},{"name":"Georg Eckert Institute for International Textbook Research Member of the Leibniz Association, 38114 Braunschweig, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8957-8954","authenticated-orcid":false,"given":"Marco","family":"Coladangelo","sequence":"additional","affiliation":[{"name":"Department of Innovation &amp; Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4256-6577","authenticated-orcid":false,"given":"Romeo","family":"Giuliano","sequence":"additional","affiliation":[{"name":"Department of Innovation &amp; Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto","family":"William De Luca","sequence":"additional","affiliation":[{"name":"Department of Innovation &amp; Information Engineering, Guglielmo Marconi University, 00193 Roma, Italy"},{"name":"Georg Eckert Institute for International Textbook Research Member of the Leibniz Association, 38114 Braunschweig, Germany"},{"name":"Faculty of Computer Science, Otto von Guericke Universit\u00e4t Magdeburg, 39106 Magdeburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cockburn, I., Henderson, R., and Stern, S. 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